scholarly journals Optimal Computational Power Allocation in Multi-Access Mobile Edge Computing for Blockchain

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3472 ◽  
Author(s):  
Yuan Wu ◽  
Xiangxu Chen ◽  
Jiajun Shi ◽  
Kejie Ni ◽  
Liping Qian ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Edge Computing ◽  
Resource Acquisition ◽  
Mobile Edge Computing ◽  
Computational Power ◽  
Mobile Terminals ◽  
Acquisition Costs ◽  
Digital Transactions ◽  
Multi Access ◽  
Computational Resources

Blockchain has emerged as a decentralized and trustable ledger for recording and storing digital transactions. The mining process of Blockchain, however, incurs a heavy computational workload for miners to solve the proof-of-work puzzle (i.e., a series of the hashing computation), which is prohibitive from the perspective of the mobile terminals (MTs). The advanced multi-access mobile edge computing (MEC), which enables the MTs to offload part of the computational workloads (for solving the proof-of-work) to the nearby edge-servers (ESs), provides a promising approach to address this issue. By offloading the computational workloads via multi-access MEC, the MTs can effectively increase their successful probabilities when participating in the mining game and gain the consequent reward (i.e., winning the bitcoin). However, as a compensation to the ESs which provide the computational resources to the MTs, the MTs need to pay the ESs for the corresponding resource-acquisition costs. Thus, to investigate the trade-off between obtaining the computational resources from the ESs (for solving the proof-of-work) and paying for the consequent cost, we formulate an optimization problem in which the MTs determine their acquired computational resources from different ESs, with the objective of maximizing the MTs’ social net-reward in the mining process while keeping the fairness among the MTs. In spite of the non-convexity of the formulated problem, we exploit its layered structure and propose efficient distributed algorithms for the MTs to individually determine their optimal computational resources acquired from different ESs. Numerical results are provided to validate the effectiveness of our proposed algorithms and the performance of our proposed multi-access MEC for Blockchain.

scholarly journals Efficient Computation Offloading in Multi-Tier Multi-Access Edge Computing Systems: A Particle Swarm Optimization Approach

2019 ◽  
Vol 10 (1) ◽  
pp. 203 ◽  
Author(s):  
Luan N. T. Huynh ◽  
Quoc-Viet Pham ◽  
Xuan-Qui Pham ◽  
Tri D. T. Nguyen ◽  
Md Delowar Hossain ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mixed Integer ◽  
5G Networks ◽  
Swarm Optimization ◽  
Multi Access

In recent years, multi-access edge computing (MEC) has become a promising technology used in 5G networks based on its ability to offload computational tasks from mobile devices (MDs) to edge servers in order to address MD-specific limitations. Despite considerable research on computation offloading in 5G networks, this activity in multi-tier multi-MEC server systems continues to attract attention. Here, we investigated a two-tier computation-offloading strategy for multi-user multi-MEC servers in heterogeneous networks. For this scenario, we formulated a joint resource-allocation and computation-offloading decision strategy to minimize the total computing overhead of MDs, including completion time and energy consumption. The optimization problem was formulated as a mixed-integer nonlinear program problem of NP-hard complexity. Under complex optimization and various application constraints, we divided the original problem into two subproblems: decisions of resource allocation and computation offloading. We developed an efficient, low-complexity algorithm using particle swarm optimization capable of high-quality solutions and guaranteed convergence, with a high-level heuristic (i.e., meta-heuristic) that performed well at solving a challenging optimization problem. Simulation results indicated that the proposed algorithm significantly reduced the total computing overhead of MDs relative to several baseline methods while guaranteeing to converge to stable solutions.

scholarly journals Co-Operative and Hybrid Replacement Caching for Multi-Access Mobile Edge Computing

2019 ◽  
Author(s):  
Emeka E. Ugwuanyi ◽  
Saptarshi Ghosh ◽  
Muddesar Iqbal ◽  
Tasos Dagiuklas ◽  
Shahid Mumtaz ◽  
...  
Keyword(s):  
Edge Computing ◽  
Mobile Edge Computing ◽  
Multi Access

scholarly journals Priority-Based Offloading and Caching in Mobile Edge Cloud

2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Fernaz Narin Nur ◽  
Saiful Islam ◽  
Nazmun Nessa Moon ◽  
Asif Karim ◽  
Sami Azam ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Mobile Network ◽  
Edge Computing ◽  
Joint Optimization ◽  
Mobile Edge Computing ◽  
Stage Of Development ◽  
Research Initiative ◽  
Novel Concept ◽  
Reducing Energy ◽  
Task Offloading

Mobile Edge Computing (MEC) is relatively a novel concept in the parlance of Computational Offloading. MEC signifies the offloading of intensive computational tasks to the cloud which is generally positioned at the edge of a mobile network. Being in an embryonic stage of development, not much research has yet been done in this field despite its potential promises. However, with time the advantages are gaining growing attention and MEC is gradually taking over some of the resource-intensive functionalities of a traditional centralized cloud-based system. Another new idea called Task Caching is emerging rapidly with the offloading policy. This joint optimization idea of Task Offloading and caching is relatively a very new concept. It has been in use for reducing energy consumption and delay time for mobile edge computing. Due to the encouraging offshoots from some of the current research on the joint optimization problem, this research initiative aims to take the progress forward. The work improves upon the “prioritization of the tasks” by adopting a very practical approach discussed forward, and proposes a different way for Task Offloading and caching to the edge of the cloud, thereby bringing a significant enhancement to the QoS of MEC.

scholarly journals Wireless Powered Mobile Edge Computing Systems: Simultaneous Time Allocation and Offloading Policies

Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 965
Author(s):  
Amna Irshad ◽  
Ziaul Haq Abbas ◽  
Zaiwar Ali ◽  
Ghulam Abbas ◽  
Thar Baker ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Time Allocation ◽  
Minimum Cost ◽  
Access Point ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Computational Power ◽  
Local Computation ◽  
Computing Systems ◽  
Battery Capacity

To improve the computational power and limited battery capacity of mobile devices (MDs), wireless powered mobile edge computing (MEC) systems are gaining much importance. In this paper, we consider a wireless powered MEC system composed of one MD and a hybrid access point (HAP) attached to MEC. Our objective is to achieve a joint time allocation and offloading policy simultaneously. We propose a cost function that considers both the energy consumption and the time delay of an MD. The proposed algorithm, joint time allocation and offload policy (JTAOP), is used to train a neural network for reducing the complexity of our algorithm that depends on the resolution of time and the number of components in a task. The numerical results are compared with three benchmark schemes, namely, total local computation, total offloading and partial offloading. Simulations show that the proposed algorithm performs better in producing the minimum cost and energy consumption as compared to the considered benchmark schemes.

scholarly journals Secrecy Offloading Rate Maximization for Multi-Access Mobile Edge Computing Networks

2021 ◽  
pp. 1-1
Author(s):  
Mingxiong Zhao ◽  
Huiqi Bao ◽  
Li Yin ◽  
Jianping Yao ◽  
Tony Q. S. Quek
Keyword(s):  
Edge Computing ◽  
Mobile Edge Computing ◽  
Rate Maximization ◽  
Multi Access
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